AI 代理人任務中樞

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the dimensions returned and notes it also returns Taiwan-available autonomous AI assistants, which is useful content-level transparency. However, it does not describe the output format, data source or freshness, or any read-only safety profile, leaving notable gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with the category tag front-loaded. It packs platform names and comparison dimensions efficiently without redundant filler, though the list is crammed rather than cleanly structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain return values. It lists the comparison dimensions and the extra autonomous-assistant data, but does not specify output format or structure (e.g., table, JSON fields), so an agent knows the content but not the shape of the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so per the rubric the baseline is 4. The empty input schema is consistent with the description's implication that the tool is a parameterless lookup/comparison.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: comparing AI agent workflow platforms by name (n8n, Make, Zapier, Dify, Coze, Copilot Studio, Agent SDK) and comparison dimensions (self-hosting, pricing, MCP support, Traditional Chinese interface). It does not explicitly differentiate this tool from siblings like compare_ai_subscriptions or get_ai_api_pricing, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The scope clearly implies when to use it (comparing agent workflow platforms), but there is no explicit guidance on when to choose it over alternatives such as compare_ai_subscriptions or get_ai_api_pricing. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the behavioral burden. It discloses that results include prices, features, and official sources, and its read-only comparison nature is self-evident, but it never confirms there are no side effects or how fresh the pricing data is.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the category tag first and the coverage scope following. No filler, though the product enumeration is long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only lookup with no output schema and no annotations, the scope and content are adequately conveyed. However, usage context and differentiation from the three adjacent aitools pricing tools are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single optional 'product' parameter is fully documented in the schema as a keyword filter, so the baseline of 3 applies. The description adds no extra semantics about how the filter matches products.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (compare) and resource (AI subscription plans) and enumerates the products covered, plus region and currency. It implicitly distinguishes itself from siblings like get_ai_api_pricing by scoping to 訂閱方案 rather than API pricing, but does not explicitly name an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description only says what the tool contains, with no guidance on when to use it versus compare_agent_platforms, get_ai_api_pricing, or estimate_ai_api_cost_twd. No exclusions or conditions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose that output is expressed in two currencies (USD and TWD) and that the required inputs vary by model modality, which is useful context. It says nothing about permissions, rate limits, or what happens with missing modality parameters (e.g. cached_tokens), leaving reasonable gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste. The purpose and currency scope are front-loaded, and the parameter-by-modality rule follows immediately. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter estimation tool with no output schema and no annotations, the description covers the essential decision (which parameters to supply per model type) and states the return currency. Minor gaps remain around cached_tokens and the exact shape of the returned estimate, but nothing critical to correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 7 parameters, so the description must compensate and it largely does: it maps each modality to its correct parameter set, clarifying which of the seven fields to populate for a given model. Only cached_tokens goes unexplained, hence not a full 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (估算/estimate) and resource (某個 AI 模型 API 的花費), and adds that the result is in both USD and TWD. An agent can tell this is a cost calculator, but it never explicitly contrasts itself with the sibling get_ai_api_pricing, so one point is held back for absent sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives invocation guidance by modality (text→input/output_tokens, image→images, video→seconds, voice→characters), which is genuinely helpful. However it offers no when-to-use versus when-not guidance and does not route the agent to or away from get_ai_api_pricing, so this stays at the implied-usage level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It usefully discloses the TWD conversion behavior and the fact that quoted rates are official vendor prices per token/image/second, but says nothing about data freshness, vendors covered, or whether network/permission constraints apply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the resource and unit scope front-loaded and no filler; only minor cost is the bracketed tag prefix.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter lookup with no output schema and no annotations, the essential shape is conveyed, but the missing vendor semantics and absent return-format hints leave gaps an agent would want filled.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It hints at the category enum by listing text/image/video/audio units, but the vendor parameter is entirely undocumented in both schema and description, leaving half the inputs unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (查詢/lookup) plus resource (AI API 官方價格) and enumerates the pricing units it covers. It implicitly separates itself from the sibling estimate_ai_api_cost_twd by being a price-table lookup, but never names or contrasts that sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the 'official price lookup' framing, but there is no explicit when-to-use guidance and no mention of the closely related estimate_ai_api_cost_twd sibling, so an agent must infer which of the two to call for a cost question.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden, yet it says nothing about what the tool returns, whether it is read-only, or any operational traits. Beyond the implicit 'get' in the tool name, no behavioral context is offered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence front-loads the Taiwan AI pricing framing and then itemizes the three topics without padding. Every clause carries information, though the absence of a verb leaves the structure slightly ambiguous.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter lookup tool with no output schema, the description adequately conveys what each topic yields. However, it omits any indication of the return format or the nature of the guidance, leaving a modest gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates meaningfully by mapping each of the three enum values to its content scope: notebooklm covers team collaboration and quotas, gemini covers Taiwan pricing and student discounts, deepseek covers government bans and enterprise compliance deployment. This gives the agent real semantic guidance for choosing the topic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description enumerates Taiwan-specific subtopics under three named enum values, which tells the agent what content is covered, but it lacks a clear verb stating that guidance is returned—it reads more like a menu than a statement of function. It is distinguishable from siblings by its Taiwan-focused, per-tool guidance framing, but the core action is only implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus related siblings such as compare_ai_subscriptions or get_ai_api_pricing; the agent must infer routing from the topic list alone. No prerequisites, exclusions, or alternative-selection conditions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does add real substance by disclosing the two rates and that it returns 實拿金額 (net amount), which is good. But it omits key behavioral context such as whether the NHI 2.11% has a threshold, how the two rates combine, and how the kind enum affects the result.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that exposes the rates and output immediately. The bracketed toolbox tag is mildly extraneous but the rest is dense and waste-free.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description must stand alone for a 2-param calculator. It conveys the calculation method and return value but leaves the kind parameter, input units, and any applicability thresholds unexplained, so it is only partially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It implies 'amount' is the endorsement income but never documents units/format, and it entirely omits the meaning of the 'kind' enum (9A vs 92) and how it changes the calculation, despite that enum being the only structured hint.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (扣繳試算/withholding calculation) and resource (網紅業配收入/endorsement income), and names the exact computation (10% professional-practice withholding + 2.11% NHI supplementary premium) plus the returned value (net take-home). This distinguishes it from siblings like calc_group_buy_profit and platform_fees, which compute different things.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The target scenario (influencer endorsement income withholding) is implied, so an agent can infer when to use it. However, there is no explicit when-to-use/when-not statement and no alternative or sibling tool is named for contrast (e.g. calc_group_buy_profit or check_tax_registration).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden, and it does disclose the calculation model (which cost components are subtracted) plus the exact set of returned metrics despite there being no output schema. For a stateless calculator this is the meaningful behavioral information. It still omits permission/auth context and any note about assumptions or defaults (e.g., what platform presets imply).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence that is well front-loaded with the toolbox tag and the core purpose, followed by deductions and outputs. Every clause carries information; only the bracketed toolbox label is pure context padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations, no output schema, and 8 parameters at 38% coverage, the description covers the return values well but leaves real gaps: the meaning of the tax enum values ('0','1','5'), whether platform presets override user-supplied commission, and the units/role of qty and cost. It is adequate to select the tool but not fully sufficient to invoke it confidently for all parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 38%, and the four undescribed parameters (qty, cost, price, platform) are never clarified in the description, so the description does not compensate for the coverage gap. It does tie the deduction categories to parameters (運費→ship, 分潤→commission, 營業稅→tax, platform 抽成→platform, 目標毛利→target), but adds no unit or semantics beyond the schema's own '每單運費' and '%' notes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: a profit/pricing estimator (試算) for group-buy and influencer direct-sale orders, and enumerates the deductions (platform commission, payment fees, shipping, revenue share, business tax) and the returned figures (net profit, gross margin, break-even price, target-margin price). This is far more specific than the bare tool name. It does not, however, explicitly differentiate itself from adjacent siblings such as creator__platform_fees or creator__calc_creator_withholding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance, and no alternative tool is named. The closest sibling, creator__platform_fees, could plausibly be confused with this one (one looks up fee rates, this one computes profit), but the description never resolves that choice for the agent. Usage is only implied by the topic.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It usefully discloses what the result contains (hit terms, risk level, penalties, acceptable alternatives), which helps. It does not state that this is a read-only/non-mutating check, nor any permission or rate-limit behavior, leaving real transparency gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that names the toolbox, the action, the detection target, and the return content with no wasted words. The bracketed prefix adds minor orientation rather than bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description steps in to describe the return value (hit terms, risk level, penalties, usable alternatives) and the detection scope, which covers most of what an agent needs. It stops short of clarifying permissions and the exact meaning of the category switch.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It implicitly maps the two parameters (the 'ad copy' is the text input, and 'food or cosmetic' corresponds to the category enum), which is a partial compensation, but it doesn't clarify acceptable text format, length limits, or how category changes results.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (check) and resource (Taiwan food/cosmetic ad copy), and names exactly what it detects (medical-efficacy / exaggerated claims). An agent understands the tool's purpose immediately, though it does not explicitly contrast itself with nearby siblings like creator__endorsement_rules or creator__listing_checklist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The bracket label and phrasing imply usage ('influencer group-buy compliance toolbox' for ad copy), giving implied context. However there is no explicit when-to-use, when-not-to-use, or named alternative, so routing vs. the endorsement/listing siblings is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral burden. It discloses the decision logic and thresholds and that it returns a registration verdict and tax rate, but says nothing about side effects, permissions, or that this is a side-effect-free calculation. Useful context, but incomplete for a zero-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with the toolbox tag and immediately states the rule applied and the output. No wasted clauses, though the bracketed toolbox prefix adds little for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with no annotations and no output schema, the description covers purpose, thresholds, and the nature of the return (registration need + rate) well enough to call it correctly. Minor gaps around the 'monthly' input semantics remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It supplies the goods/services thresholds that map to the 'type' enum and the comparison basis for 'monthly' (當月), but never clarifies that 'monthly' is the monthly sales figure or its currency/units, leaving real gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource (判斷網路賣家是否需辦稅籍登記) and states the exact legal thresholds it applies (貨物 10 萬、勞務 5 萬), making the purpose concrete. It is distinguishable from creator siblings like calc_creator_withholding or platform_fees, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance and no named alternatives. The scope (online sellers, monthly determination) is implied by the text but the agent is left to infer the triggering context on its own.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the scope of content (disclosure obligations, advertiser penalties, joint liability), which is useful, but never states that this is a static reference lookup returning textual regulation summaries, nor whether it is read-only or has any side effects. The topic list is informative but the behavioral profile is incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with no filler. The bracketed toolbox label is a category tag that adds mild context at little cost, but it is arguably the only non-essential element.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-argument knowledge tool with no output schema and no annotations, the description conveys the subject matter but says nothing about the shape of the returned content (e.g., a text summary vs. structured rules). It is adequate but leaves the agent to guess the return form.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. There is nothing for the description to clarify beyond the empty schema, and it correctly implies no inputs are needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource (台灣公平會薦證廣告規範) and enumerates the three topics covered (揭露義務、廣告主罰則、網紅連帶責任), so the agent knows exactly what reference content to expect. The verb is only implied (it returns/explains rules) and it does not explicitly distinguish itself from the sibling creator__check_ad_claims, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance, no trigger conditions, and no mention of the closely related sibling creator__check_ad_claims. The agent must infer usage entirely from the domain label.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden, and it says nothing about what the tool returns, whether it is read-only, or how the checklist output is structured. The only behavioral hint is that categories is multi-select, which is thin for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler, context front-loaded in brackets and the parameter guidance immediately after. Efficient, though the bracketed prefix is somewhat decorative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only one optional parameter and no output schema, the description covers the category options adequately, but it omits return-value expectations and multi-select syntax. For a checklist generator with no annotations to fall back on, this leaves some invocation detail unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the single parameter is just typed as a plain string, so the description must compensate. It usefully enumerates the six allowed category values (food, cosmetic, electric, toy, medical, general), which is real added value, but it does not specify the input format for multi-select (comma-separated vs. repeated), leaving invocation ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific deliverable ('compliance checklist before launching a group-buy') within a named toolbox ('influencer group-buy compliance toolbox'), so an agent can tell it produces a checklist distinct from siblings like check_ad_claims or endorsement_rules. It stops short of explicitly contrasting those siblings, so it's clear but not fully differentiating.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase '開團上架前' (before listing a group-buy) implies the timing/context for use, giving partial guidance. However, it never states when NOT to use it or names alternative creator__ tools for overlapping compliance questions, leaving the agent to infer routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose what is retrieved (registration status, representative, capital, address), which is useful since no output schema exists. It omits error behavior for an invalid 統一編號 and any auth or rate-limit context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single colon-structured sentence with the toolbox label, the operation, and the retrieved fields all front-loaded. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only lookup with no output schema, the description explains what comes back, so an agent knows the shape of the result. It falls short on the two undocumented parameters, especially the unmentioned name-based search, and on failure modes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and neither 'ban' nor 'name' has a schema description. The description maps 統一編號 to the cryptic 'ban' parameter (check-digit verification), which is genuinely helpful, but the 'name' parameter is never addressed, leaving half the inputs undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: look up a Taiwan company, verifying the 統一編號 check digit and pulling MOEA registration data. The named output fields (registration status, representative, capital, address) make the scope unambiguous and distinct from every creator__ sibling, none of which perform company registry lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 「網紅開團合規工具箱」 tag implies this is the compliance-track lookup, giving implicit usage context. However, it never states when to reach for this versus relatives like creator__check_tax_registration, nor when it should not be used (e.g., non-Taiwan entities).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses meaningful scope (which platforms, which fee types, which year, and the self-hosted comparison baseline), which is genuinely useful framing for a read-only reference tool. It does not say whether figures are estimates or live, how current the 2026 rates are, or what form the answer takes, so the disclosure is partial rather than complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the bracketed toolbox tag front-loading the domain and the key scope (platforms, year, fee types) immediately after. It is efficiently sized, though the bracketed category prefix carries little decision-making value compared to the substantive content that follows.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, no parameters, and no annotations, the description should tell the agent enough about the returned content to know whether it answers the question. It does indicate the covered platforms and fee categories, but says nothing about data currency, granularity (per-platform breakdown vs single comparison), or limitations, leaving a gap for a lookup tool whose entire value is the returned reference data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters and the schema is empty, so the description has no parameter semantics to explain. Baseline for a no-parameter tool is 4; the description also usefully signals what dimensions the fixed output covers (platform type, commission vs payment-flow fees).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource — 2026 commission (抽成) and payment-flow rates (金流費率) for Taiwan e-commerce platforms (Shopee general sellers and malls) — with a stated comparison against a self-hosted site, which separates it from the sibling calculators like creator__calc_group_buy_profit. It is a noun phrase with no explicit verb, so the action (look up / compare reference rates) is inferred rather than stated, but the content is unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 對比自架官網 implies a use case — evaluating platform fees against running your own store — but there is no explicit when-to-use, no exclusions, and no routing to alternatives. With numerous siblings covering creator profit, withholding, and tax concerns, the definition leaves the agent to guess when this reference is the right one.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden and does well by disclosing the authoritative data source and, crucially, the limitation that absence from the list is not a guarantee of safety. It does not describe rate limits, freshness, or the exact return shape, but the key behavioral caveat is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tightly written sentences: the scope/source is front-loaded and the caveat follows. Every clause earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple single-parameter lookup with no output schema, the description covers source, purpose, and the meaning of a negative result. It could say slightly more about what a positive match returns (flags, categories), but nothing essential for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single `url` parameter is already documented with an example in the schema, so the description adds no format or syntax detail beyond it. Baseline 3 for a fully-covered single-parameter schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a precise verb and resource: it checks a URL/domain against the specific 165 'stopped-resolution fraud website' list published by Taiwan's National Police Agency. The data source is named, so the agent knows exactly what kind of check this is and can distinguish it from other crypto tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly implies the context of use (screening crypto-related URLs/domains for fraud) and gives an important interpretive rule: a negative result does not prove safety. It does not name competing alternatives, but no sibling performs a comparable scam-domain lookup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose the computation's outputs (basic tax amount and amount payable), implying a pure, non-destructive calculation, but it omits the assumptions that drive the result (AMT threshold, inclusion rules for overseas income) and any prerequisite conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single well-formed sentence, front-loaded with the bracketed Taiwan-crypto scope tag followed by the computation described. No filler, though the sentence is dense enough that structure is only adequate rather than exemplary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description usefully names what the tool returns (basic tax amount and payable amount), and all four inputs are documented in the schema. For a deterministic tax calculator this is close to sufficient, missing only the tax-law assumptions behind the computation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and all four parameters carry Chinese explanations, so the schema already does the heavy lifting. The description adds no extra semantic detail about the parameters, matching the baseline 3 for a fully documented schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (試算/estimate) and a precise resource (Taiwan individual overseas income minimum-tax-regime basic tax and payable amount in NT$), and scopes the inputs to overseas exchange crypto gains. It is clearly distinguishable from sibling crypto tools and from creator/stocks tax calculators, though it does not explicitly name an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus alternatives, nor any prerequisite or exclusion (e.g. income-threshold conditions). Usage is only implied by the topic, which is the minimum-viable signal rather than actual guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It adds useful behavioral context by naming the exchanges (BitoPro, MAX) and the benchmark (international USD/TWD midpoint), and it indicates the output includes a latest premium and daily historical records. However, it omits data freshness, update frequency, return format details, and any limitations, which a no-annotation definition should ideally cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with a topic tag; no redundant or filler text. It communicates the full scope efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one simple parameter, no output schema, and no annotations, the description adequately explains what the tool returns (latest premium and daily history) and the data sources. It could specify the return structure or data update cadence, but it is sufficient for calling the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter 'days' is fully documented in the schema (default 30, max 730). The description does not mention the parameter or add any syntax beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('取得') and resource (USDT/TWD premium on BitoPro, MAX relative to international USD/TWD midpoint), including the exact metric and history scope. It is clearly distinguishable from all siblings, none of which cover this premium data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance or alternatives are provided. It does not say when this data is relevant versus trip__exchange_rates or other crypto tools. Usage is only implied by the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It does disclose the authoritative source (FSC announcement) and the scope of records, which is genuinely useful, but it says nothing about data freshness, whether the list is exhaustive, or how the fee figures are sourced or dated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence, bracketed domain tag first, then resource and returned fields — fully front-loaded with no filler. The trailing mention of platform fees is slightly tangential but still part of the payload.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter listing tool with no output schema, the description does the necessary work by naming the data source and enumerating the returned fields. Only the currency/format of the fee figures and the list's update cadence are left unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there are no parameter semantics to document; baseline 4 applies. Nothing in the description misleads about inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('列出' / list) and a precise resource (VASPs that have completed AML registration per Taiwan's FSC), and enumerates the fields returned (company, tax ID, brand, service type, platform fees). No sibling in the crypto group covers registered-VASP data, so it is easy to place, though it does not explicitly name what it is not.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent can infer this is the tool for looking up which Taiwanese crypto exchanges are legally registered. There is no explicit when-to-use, no exclusion, and no pointer to a sibling (e.g. check_scam_domain or get_usdt_twd_premium) for the adjacent questions a user might actually have.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations, so the description carries the full burden. It adds one genuinely useful behavioral detail — each entry ships with the original announcement link — but says nothing about read-only nature, result volume, time window, or ordering. Adequate but thin for a no-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence, front-loaded with the bracketed domain label and then the filter criterion. No filler, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters, no output schema, and no annotations, the description still conveys what is returned (schedule changes plus their announcement source). Enough to call it correctly, though the absence of any time-range or ordering statement leaves a small gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to explain beyond what the empty schema already shows. Baseline 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific scope: games in the release-date/trailer database that had an official schedule change (delayed or moved up). A clear verb+resource combination that an agent can distinguish from games__release_calendar or games__get_game, though it never names those siblings to sharpen the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied — the agent can infer it should call this when it wants to know which titles slipped or advanced. There is no explicit when-to-use guidance and no mention of the near-identical screen__schedule_changes sibling, leaving routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full behavioral burden. It usefully discloses the breadth of returned data (release schedule, delay history, videos, news links), but says nothing about auth, rate limits, error cases, or whether the result is a single record vs. a ranked list. Adequate but incomplete for an unannotated tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense sentence front-loaded with a bracketed database tag followed by the operation and its output fields. No filler; every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description must carry the return-value and safety load. It does list the data fields, which partly substitutes for an output schema, but omits behavioral constraints and leaves the sibling ambiguity (lookup_any_game) unresolved.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% – the single parameter 'q' is entirely undocumented in the schema. The description compensates by explaining that Chinese or English names both work, with concrete examples (GTA VI, 俠盜獵車手VI), which materially shapes how the agent fills that parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (查/look up) and resource (單一遊戲/single game) and enumerates the returned data types: developer/publisher, platforms, first-public date, release schedule and delay history, trailers, news links. It is clear what it does, but it never distinguishes itself from the near-duplicate sibling games__lookup_any_game.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no routing to alternatives. The phrase 查單一遊戲 implies lookup semantics, but with siblings like games__lookup_any_game and games__list_games present, the absence of any disambiguation leaves the agent guessing which tool to pick.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It discloses the domain scope and which fields appear, implying a read-only listing, but says nothing about result limits, pagination, ordering, or whether an unknown filter yields an empty set or an error.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with the domain label and then the contents and filters. No filler, though packing the field list and filter list together makes it slightly dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter tool with no annotations and no output schema, the description covers scope and most filters but omits the 'upcoming' parameter and any return-value or ordering expectations, leaving real gaps for an agent to guess at.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It loosely maps to platform, status, and date (from/to) filters, but leaves the 'upcoming' boolean unexplained and gives no date format or enum-value guidance, so coverage remains partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb (list) and resource (games) with explicit scope (2026–2027 titles across PS5/Xbox/PC/Switch 2/mobile) and the fields returned (announcement date, release date, status). It does not distinguish itself from near siblings like games__get_game, games__lookup_any_game, or games__release_calendar, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description notes the tool supports filtering by platform, status, and date, which implies when it is useful (browsing a filtered set), but it never names an alternative or a when-not-to-use condition against the many sibling lookup/calendar/countdown tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden, and it does disclose the data source (Wikidata public data), which usefully signals an external read-only lookup whose coverage and freshness depend on an upstream dataset. However, it says nothing about behavior when a game is not found, language/localization behavior, or any rate/auth constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that is front-loaded with the domain tag and then the action and payload. Lean and free of filler, though the bracketed database label is slightly promotional rather than informational.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description appropriately enumerates the returned fields, which compensates for that gap. But with no annotations and an undocumented sole parameter, the definition remains incomplete on the input side.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single required parameter, which is cryptically named 'q'. The description implies 'q' is a game, but does not state whether it expects a title string, a localized name, or a Wikidata QID, nor anything about matching rules — leaving the only parameter ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (lookup) and resource (any game) and enumerates the returned fields — release date, platforms, developer, official site, YouTube videos — which is concrete and verifiable. The bracket label '[Game release date and trailer database]' frames the domain. It stops short of naming or contrasting with siblings like games__get_game or games__list_games, so the agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance. The phrase 'any game' hints at broad coverage (presumably a fallback for titles not in a curated list), but the description never names an alternative tool or a condition that selects this one over games__get_game / games__list_games, both of which return overlapping data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations, so description bears full burden. It discloses that the dataset includes both dated and undetermined releases, but does not cover read-only nature, optional parameter behavior, or return format. Adequate but with gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One concise sentence, front-loaded with the core purpose and an extra scope note. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup with one optional param and no output schema, the description covers the content but omits what happens if month is omitted and any return structure hints. Adequate but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single optional month parameter (YYYY-MM). The description adds no parameter details, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States it provides a game release calendar by month and includes undetermined-period titles. Clear resource and scope, though it does not explicitly differentiate from sibling games tools like release_countdown or list_games.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance or mention of alternatives. The agent must infer appropriate usage from the tool name and sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden. It does reveal one genuinely useful behavioral trait — the default target is GTA VI when no argument is given — but says nothing about ambiguous names, unknown games, error behavior, or the shape of the answer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single tight sentence with the category tag and the core behavior front-loaded. Nothing is wasted, though the bracketed database label is closer to branding than to instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-annotation, one-optional-parameter tool with no output schema, the description covers the basic contract but omits what the return value looks like (a day count) and how misses are handled. Adequate but with visible gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single parameter 'q' is never described. The mention of a GTA VI default only weakly implies that q accepts a game name; nothing indicates whether it takes a fuzzy title, a slug, or a localized name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific computation (days remaining until a game's release) on a specific resource (game release/trailer database), which is more precise than the bare name. It does not, however, distinguish itself from the closely named sibling games__release_calendar or screen__release_countdown, leaving the boundary to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance and no named alternative, even though several sibling tools (release_calendar, get_game, lookup_any_game) plausibly overlap. The only usage signal is the implied default of GTA VI when no query is supplied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the output payload (suggested answer structure, structured data type, related content), which is meaningful, but it omits return format details, determinism/confidence behavior, and any read-only/safety context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence with the classifying verb early and the output facets trailing. The bracketed tagline is slightly redundant but the whole is compact and wastes little.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a classifier with no output schema and no annotations, the description adequately conveys what comes back, but leaves the parameter's expected shape and the exact return structure under-specified for an agent to rely on.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter q has 0% schema description coverage, so the description must compensate. It implies q is 'a search sentence or question' (一句搜尋或提問), which adds some semantic meaning, but gives no format, length, or language expectations – partial compensation only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (判斷/classify) and resource (a search or question), and enumerates the four intent categories it resolves to. This is clearly distinct from glossary siblings like explain_term or get_howto, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states what the tool classifies but gives no when-to-use condition, no prerequisites, and no routing against alternatives such as search_glossary or explain_term. The agent must infer that this is the entry point when an unclassified query arrives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It usefully discloses the return content (結論、比較表、適用情境), which is genuine behavioral context for an informational tool, but it says nothing about permissions, rate limits, or how the id/term lookup resolves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences, front-loaded with a bracketed domain label and then the action, with zero filler. Efficient, though the bracketed tag and parenthetical examples are slightly dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description does cover the return shape and the two input modes, which is the essential information. However, the meaning of the optional 'id' parameter and the choice against neighboring glossary tools are left unresolved, so it is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema adds no parameter meaning. The description partially compensates by explaining the two input modes ('可給 id，或 a、b 兩個名詞'), clarifying the either/or relationship between id and a/b, but it never defines what an 'id' refers to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (比較／compare) and resource (兩個 AI 概念／two AI concepts), plus concrete pairs (RAG vs 微調, MCP vs API, SEO vs GEO) that make the scope unmistakable. This inherently distinguishes it from glossary__explain_term, which handles a single term, so an agent can pick correctly without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The examples imply when this tool is appropriate (pairwise concept comparison), which is useful, but there is no explicit statement of when to use it versus glossary__explain_term or glossary__search_glossary, and no when-not guidance. Usage is implied rather than directed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden, and it does disclose the return content (definition, mechanism, examples, misconceptions, FAQ, sources) and the accepted input forms (Chinese, English, aliases). It does not state error behavior when a term is missing or the scope of covered terms.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with a bracketed summary tag followed by one dense sentence that covers inputs and outputs. Efficient with no redundant sentences, though the bracketed label duplicates the body slightly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only lookup with no output schema and no annotations, the description adequately explains what comes back and what inputs are accepted. It could be stronger by stating a fallback when a term is not found, but nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds genuine meaning: it confirms the term may be given in Chinese, English, or an alias, matching the schema's id-style examples. It slightly exceeds the schema's own example list rather than merely repeating it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (explain) and resource (AI/tech term) and enumerates concrete examples (MCP, RAG, GEO, x402) plus the return payload (definition, mechanism, examples, misconceptions, FAQ, sources). It does not explicitly distinguish itself from its glossary siblings such as search_glossary or compare_concepts, though the 'full explanation' framing implies the difference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent can infer you call this when you need a plain-language explanation of a term. There is no explicit when/when-not statement, no naming of alternatives like search_glossary or compare_concepts, and no stated prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose the shape of the returned content (requirements, steps, code, common mistakes), which is useful, but says nothing about lookup behavior, whether both q and id are honored, or whether results are cached/static.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence: the bracketed category tag then the action and the covered topics. The parenthetical topic list is long but informative rather than redundant, and no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description does describe the response contents, which partially covers the gap. But it leaves the two-parameter contract (q vs id) unexplained, which is the main thing an agent needs to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%: q has a description with an example, but id is bare with no explanation. The description lists topics but does not clarify the q-vs-id relationship or whether one takes precedence, so it fails to compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb (get) plus a specific resource (AI implementation tutorials), with concrete covered topics (MCP server, llms.txt, FAQPage JSON-LD, robots.txt, x402, token counting). However, it never differentiates itself from close siblings like glossary__explain_term or glossary__compare_concepts, so an agent must infer the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives such as explain_term (definitions) or compare_concepts. The topic list implies the domain but leaves routing between glossary siblings to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description must carry the full behavioral burden, and it does not. It never states that this is a read-only enumeration, whether results are paginated or bounded, how large the full list is, or what a returned topic entry contains.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the scope tag in brackets and no filler. It is tight, though the trailing clause about comparisons and tutorials is arguably wasted words that belong to sibling tools.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No annotations, no output schema, and only one param leave the description as the sole source of information, yet it omits return shape, list size, and the boundary against glossary__search_glossary. For a listing tool with several close siblings, that is too thin to call it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description's '可依分類篩選' is the only prose tying the category parameter to filtering behavior. The six enum values are self-explanatory labels, so the description adds modest but real value; it does not explain whether omitting category returns everything or defaults to a subset.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The verb+resource ('列出所有名詞', list all terms) is identifiable, and the category-filter capability is stated. However, it also claims to deliver '概念比較與實作教學' (concept comparisons and how-to tutorials), which overlaps directly with the sibling tools glossary__compare_concepts and glossary__get_howto, muddying what this tool uniquely returns rather than clarifying it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No condition is given for choosing this over glossary__search_glossary, glossary__explain_term, or glossary__compare_concepts. The parenthetical only describes a filter mechanism, not when a caller should reach for a full listing instead of a search or lookup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose that search is fuzzy (模糊) and scoped to the site, but says nothing about result format, ranking behavior, pagination, limit behavior, or any other operational detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence, front-loading the content scope before the action. It avoids repetition and waste, though its brevity contributes to gaps elsewhere rather than being a conciseness problem itself.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter search tool with no annotations, no output schema, and zero schema description coverage, the description is too thin. It does not explain parameter usage or distinguish this tool from overlapping glossary siblings, which an agent needs in order to select it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not mention either parameter. It does not explain what q should contain or how limit affects the search, leaving both parameters entirely undocumented in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (模糊搜尋 / fuzzy search) and resource (本站的 AI 名詞、概念比較與實作教學 / this site's AI terms, concept comparisons, and implementation tutorials). It is clear what the tool does, but it does not differentiate from sibling glossary tools such as explain_term, compare_concepts, or get_howto.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus its glossary siblings. The description implies it is a search tool, but gives no conditions, exclusions, or alternative-routing advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It usefully discloses the return content (whether the value is in general control range plus hypoglycemia handling) for a read-only interpretation, but says nothing about permissions, limits, or how the 'general target' is derived.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence, front-loaded with the tool's purpose and qualified by the parameter modes and output content; nothing is wasted, though it crams several clauses together.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and 0% schema coverage, the description does the necessary work by naming the input unit, the enum meanings, and the return content. Adequate for a simple two-parameter lookup, though it could state validity assumptions for the value range.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema does not explain either parameter. The description compensates by glossing the enum values (fasting=飯前, after_meal=飯後) and stating the unit for value (mg/dL), adding real meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (判讀/interpret) plus resource (單次血糖值, single blood glucose reading) with units (mg/dL), and names the qualifying input mode. This cleanly separates it from sibling health tools like check_home_blood_pressure and check_cancer_screening_eligibility.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the fasting/after_meal distinction, which implies how the tool is used, but gives no explicit when/when-not or pointer to any alternative for related health questions. Usage is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool lists screenings based on criteria, which implies a read-only operation, but it does not explicitly confirm no side effects, rate limits, or the nature of the output. Key behavioral traits beyond basic purpose are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the tool's scope, inputs, and output type without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no annotations and no output schema, the description is minimal but covers the core purpose and most input factors. It does not explain the return structure or the two undocumented parameters, leaving some gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description is the only source of parameter meaning. It mentions age, sex, smoking, betel, and family history (covering roughly 6 of 8 parameters semantically), but omits indigenous and pack_years entirely. Given the low schema coverage, this partial compensation is insufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (列出, list) and resource (公費癌症篩檢, government-funded cancer screenings) scoped to Taiwan year 115 (2026). It implies eligibility logic based on inputs, distinguishing it from generic cancer-risk tools. However, it does not explicitly differentiate from the closest sibling (health__check_meal_cancer_risk), so a 4 is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description mentions input factors but does not say in what situations an agent should invoke it. This is a straightforward informational lookup with no context given for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does reasonably well: it discloses the reference standard (130/80), the returned outputs (average, classification, critical-value flag, 722 method), and the input constraint (max 6 readings). It does not describe error handling for malformed input, which keeps it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence, front-loaded with the framework and standard, followed by the return contract and input example. Little waste, though the packing of several facts into one clause-chain slightly reduces scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-input classification tool with no output schema, the description covers the input format/limit, the evaluative standard, and the shape of the result. That is sufficient for an agent to call it correctly; only edge-case behavior is left unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: it gives a concrete format example ("128/82,134/86") and a cardinality limit (最多 6 筆) that the bare string schema does not express. This meaningfully clarifies the single parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb+resource: "判讀居家血壓" (interpret home blood pressure), and it names the governing standard (Taiwan 2022 guideline, 130/80). This clearly distinguishes it from the sibling health__check_blood_glucose, which handles a different metric.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case (interpreting home BP readings against a guideline) but gives no explicit when-to-use, when-not-to-use, or alternative routing. Usage is inferred rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the disclosure burden. It usefully reveals the input language constraint (Chinese) and the shape of the output (risk items plus improvement suggestions), which is more than nothing, but it omits any note on permissions, limits, or whether results are informational versus diagnostic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence, bracket-tagged with its domain (Taiwan health info) and front-loaded with the input requirement before the processing logic. Dense but not padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema, the description covers both the input format and the nature of the returned content (risk items and improvement suggestions), which is sufficient for correct invocation. Only minor gaps around data sourcing or caveats remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, and it does: it clarifies that the single 'meal' parameter accepts either one meal or a full day's food and must be supplied in Chinese. That adds real semantic meaning beyond the bare string-typed schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — assessing a meal's cancer risk by comparing it against IARC classifications — and enumerates the risk categories it flags (processed meat, red meat, alcohol, betel nut). This is clearly differentiable from siblings like check_cancer_screening_eligibility, which concerns screening eligibility rather than dietary risk analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it (when you have a meal or daily food intake to assess for carcinogenic risk) and constrains the input format, but it never states when NOT to use it or names an alternative tool such as the screening-eligibility sibling. Usage is inferable but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral disclosure burden. It usefully tells the agent what information the guide covers, but it does not state that the tool is read-only, that it returns static guidance rather than live counseling, or what form the response takes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence with the health-information context front-loaded and the covered topics listed efficiently. Every clause earns its place by telling the agent what content the guide supplies.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter informational tool with no output schema, the description is largely complete: it specifies the jurisdiction and the exact topics covered. It stops short of stating the return format or distinguishing the guide from nearby crisis-line and telehealth-eligibility tools, but the core content scope is clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero input parameters, and the schema is an empty object. Per the baseline for parameterless tools, there is nothing further the description needs to clarify about parameter meanings.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource and enumerates its contents: Taiwan online counseling legal conditions, age limits, the 15–45 free three-session program, booking steps, and crisis hotlines. This makes the tool's purpose clear without opening a schema, but it does not explicitly distinguish itself from sibling tools such as wellness__crisis_lines or wellness__check_telehealth_eligibility.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists topics covered but gives no explicit guidance on when to use this tool versus alternatives. It does not mention when-not to use it or point to a related sibling for crisis lines or telehealth eligibility checks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose real behavioral scope: it is an informational lookup bounded to three named drugs and explicitly excludes dosing advice. Safety-relevant content (severe warnings, when to seek medical care) is flagged. It still does not say whether the content is static reference material or how current it is.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence: domain tag first, then the covered content, then the hard exclusion. Dense but every clause adds a distinct content category; only the parenthetical drug list makes it slightly heavy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no parameters, the description is essentially the whole contract, and it does define what the returned page covers plus its boundary. Missing minor items: whether the three drugs are exhaustive and any freshness/source-date caveat.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4 and there is nothing for the description to disambiguate. It correctly implies a no-input retrieval rather than misleading the agent into expecting filters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (Taiwan-approved GLP-1 weight-loss drugs) and enumerates exactly what it returns: incidence rates, serious warnings, when to seek care, TFDA reminders. It even names the individual drugs (tirzepatide, semaglutide, liraglutide), which disambiguates it from the other health__* tools at a glance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The subject matter makes the trigger obvious (a question about GLP-1 side effects), and the closing clause '不提供劑量建議' explicitly carves out a when-not case. However, it names no alternative sibling to route dosage or general-medication questions to, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral burden. It discloses what is returned (best tool, call example, external servers) and that the input must be in Chinese, which is useful. It does not state determinism, latency, or whether results are cached/curated, leaving meaningful gaps for a discovery tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that front-loads the input format ('用中文描述任務') before the returned payload and examples. Nothing is wasted, though cramming input guidance, examples, and output description into one sentence is dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description at least names the three components of the response (recommended tool, call example, external MCP servers), which is adequate for a routing tool. The exact shape/format of the recommendation is not described, but coverage is reasonable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for the single 'task' parameter, so the description must compensate. It specifies that the value is a Chinese-language description of the task and supplies three concrete examples (0056 dividend, invoice lottery, 2027 CNY leave), which clarifies the expected input far beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific function: given a Chinese-language task, return the most suitable Taiwan data tool, a call example, and reference external MCP servers. It is distinguishable from siblings like hub_search_mcp_servers and hub_search_x402_apis, which search servers/APIs rather than routing tasks to tools. Purpose is clear though the tool's meta/routing nature is not explicitly framed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied (use this when you have a task but don't know which tool applies), and the concrete examples of task phrasing help. However, it never states when to use this versus hub_search_mcp_servers/hub_search_x402_apis, nor any exclusions or prerequisites, so the routing guidance stays implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the data source (the official MCP Registry), which is useful context, but does not state that this is a read-only lookup, mention rate limits, or describe result volume/pagination. For a search operation this is a moderate gap rather than a severe one.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and filter dimensions are front-loaded in the first sentence, and the category list is long but each entry adds value as the enum of valid 'tag' values. No filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no annotations and no output schema, the description covers purpose, filterable fields, and the category vocabulary. Return-value explanation is unnecessary without an output schema, and only the absent usage guidance holds it back from completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, and it does for three of four parameters: keyword (q), category (tag), and remote-connection (remote_only). It even enumerates the acceptable category values, which the schema lacks entirely. Only 'limit' is left unexplained, which is largely self-evident.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: '搜尋官方 MCP Registry 的 MCP 伺服器' (search MCP servers in the official MCP Registry), with the filterable dimensions spelled out. It does not explicitly contrast itself with hub_find_tool_for_task, but the resource (a registry of servers) is concrete and distinct enough to be actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists filter dimensions but gives no guidance on when to use this tool versus alternatives such as hub_find_tool_for_task, nor any prerequisites. Usage is only weakly implied by the mention of filters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It does disclose the domain (x402 Bazaar) and what the results cover (price, network, description), which implies a read-only search, but it says nothing about result limits, pagination, or whether listing implies purchasing. Adequate but thin for a no-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence with the resource front-loaded and the returned fields parenthesized at the end. No wasted words, though it is very terse given the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A search tool with 2 params, no annotations, no output schema, and 0% schema description coverage. The description does not explain query semantics or result pagination, leaving meaningful gaps for an agent trying to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for the two parameters (q, limit), and the description never explains what q matches or what limit controls. With 2 undocumented params the description should compensate but does not add any parameter meaning beyond the schema's bare types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (搜尋/search) and resource (x402 Bazaar APIs payable per-call with USDC), and names the returned fields (價格、網路、說明). It is distinguishable from the sibling hub_search_mcp_servers by scoping to x402 paid APIs rather than MCP servers, though it does not name any sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use or when-not guidance is given, and the closely related siblings hub_search_mcp_servers and hub_find_tool_for_task are never referenced. The agent must infer that this is the search tool for paid APIs on its own.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It helpfully discloses the return payload (win status, prize tier, amount, redemption window), which is real behavioral value. It says nothing about data source, freshness/latest-period behavior, or any lookup limits, so the disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence with a bracketed category tag; the input requirement and output content follow immediately. Nothing is wasted, though the tag prefix adds only marginal value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly enumerates what is returned, and the two-parameter input is fully covered by the schema. For a simple read-only lookup this is nearly complete; only freshness/source and any caveats about non-winning or invalid numbers are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters are already documented (number = 8-digit invoice number, period defaults to latest). The description restates the 8-digit constraint but adds no syntax, format, or period-handling detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb (對獎/check) and resource (統一發票), plus the expected input and the returned fields (中獎與否、獎別、獎金、領獎期間). However, it does not explicitly distinguish itself from close siblings such as life__get_invoice_numbers or life__check_lottery, so it stops short of the top band.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use it when you have an 8-digit invoice number and want to know if it won. There is no explicit when-not guidance and no named alternative (e.g. life__check_lottery or life__get_lottery_results), leaving the agent to infer routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose the response shape (matched count, prize, per-ticket prize, redemption deadline), which is useful since there is no output schema, but it omits constraints such as which games require the 'second' zone or whether a draw period must already be released.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence, front-loaded with the category tag and the input/output contract. Nothing is wasted, though the bracketed tag adds little information value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-param tool with no annotations and no output schema, describing the return values is a genuine plus, but the undocumented 'period' parameter and the absent routing to life__get_lottery_results leave it only partially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 25% (only 'second' is documented), so the description must compensate. It names 'game' and 'numbers' but says nothing about 'period' (the draw cycle) – that parameter is undocumented in both the schema and the description, leaving a real gap for a 4-param tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (對彩券/check) and resource (lottery), and even enumerates the return values (matched count, prize tier, per-ticket prize, redemption deadline). It is clear what the tool does, but it never distinguishes itself from the sibling life__get_lottery_results, which likely serves the winning-numbers lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by 'input game and numbers' – the agent can infer this is for checking a user's own ticket, but there is no explicit when-to-use, no prerequisite, and no mention of the sibling life__get_lottery_results as the alternative for fetching drawn numbers.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the disclosure burden. It is transparent about what the response contains (fuel grades, unit, week-over-week change), which is valuable given there is no output schema, but it says nothing about data source cadence, staleness, or the read-only nature of the call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with a bracketed category tag, then the resource and its full scope. No filler, nothing repeated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters, no annotations and no output schema, the description is the sole carrier of information, and it does cover the returned fields, unit and comparison basis. The only omission is refresh timing/source for the weekly figures.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. The description still adds worth by enumerating the fuel grades and currency the caller will receive, information the empty input schema cannot convey.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (get), resource (Taiwan CPC weekly fuel prices) and enumerates exactly which products are covered (92/95/98 unleaded, ethanol gasoline, super diesel) with units (NTD per litre). No sibling in the catalog covers fuel prices, so an agent can identify this immediately.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 『本週油價』and 『比上週漲跌』implicitly scope the tool to the current week's published prices plus week-over-week delta, which tells an agent not to use it for historical lookups. However, there is no explicit when-to-use statement, no freshness caveat (e.g. when the weekly update lands), and no named alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries more burden, but it does disclose the data source (人事行政總處辦公日曆) and what is included, such as today's holiday status and the next long weekend. It does not describe output format, error behavior, or read-only status explicitly, though '取得' implies a read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single well-structured sentence that front-loads the purpose, then adds source and content scope. Every element is relevant and there is no unnecessary repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only query tool with one optional parameter and no output schema, the description gives enough context about what is returned: holidays, long weekends, make-up days, today's status, and the next long weekend. It is slightly incomplete because it does not help disambiguate from sibling holiday tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is one optional parameter and schema description coverage is 100%, so the schema already explains the year parameter and its default behavior. The description adds no parameter-specific meaning beyond what the schema provides, making the baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: retrieving Taiwan national holidays, long weekends, and make-up work days based on the official government calendar. It also notes it includes today's holiday status and the next long weekend. However, it does not explicitly distinguish itself from related siblings such as life__is_holiday or trip__taiwan_holidays.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit when-to-use or when-not-to-use guidance, and it does not mention alternatives. An agent must infer that this is for querying Taiwan holiday data, with no routing help against nearby tools like life__is_holiday.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden, and it does disclose the payload contents (special/grand/first prize numbers and the redemption window). It does not state that this is a read-only public lookup, nor anything about caching, freshness, or what happens when a period has no data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the resource named up front and the returned fields enumerated immediately after. The bracketed category tag adds slight noise but also aids scanability; nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still communicates what the caller receives (prize numbers by tier plus the claim period). Combined with a fully documented parameter, an agent has enough to call it correctly; only the freshness/emptiness behavior is unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single optional 'period' parameter with its example format and the 'latest period if omitted' default is already fully documented in the schema. The description adds no parameter detail, which is acceptable given the coverage baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (取得) and resource (台灣統一發票中獎號碼), and enumerates the prize tiers returned plus the redemption period. An agent can tell this apart from life__get_lottery_results or life__check_invoice by the invoice/prize-number scope, though no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance and no mention of alternatives, which matters here because life__check_invoice (checking your own invoice) and life__get_lottery_results are near-neighbours. Usage is only inferable from the purpose statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It says '即時查詢' (real-time query) but discloses nothing about data source, freshness/refresh cadence, caching, or error behavior for non-existent periods. For a read-only lookup this is thin, though low risk.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence covering the resource, the games, and the returned fields, with zero filler. Efficient and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter lookup with no output schema, the description covers what is fetched but not the shape of the response (e.g., per-game prize tiers) or the behavior when a period is missing. Adequate but leaves the return contract implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50%: the period parameter is self-documented with its default ('不填為最新'), and the game enum values are self-explanatory. The description names the three games in Chinese but adds no mapping to enum values or format details, so it does not materially exceed the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('取得...開獎號碼與各獎項獎金') and names the three lottery games covered, so the agent knows what data comes back. However it does not distinguish itself from the sibling life__check_lottery, which reads as the same domain and could easily be confused with it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use, when-not-to-use, or alternative guidance. The presence of a near-identical sibling (life__check_lottery) makes the absence of routing guidance a real gap, since the agent has no signal for choosing between them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose a genuinely useful trait beyond the schema: the answer accounts for 補假 and 補班 days, so the result is not a naive calendar lookup. However, it says nothing about the response shape (boolean vs. holiday name), data source freshness despite the 即時 label, or any rate/permission constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the resource and scope front-loaded; the bracketed category tag is conventional and adds no bloat. Nothing here is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one simple required parameter and no output schema, the description covers the core action adequately. It falls short on the two things an agent would still need: what the return value looks like (boolean, holiday name, or text) and how this differs from the sibling holiday-listing tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter exists and the schema already documents it fully (YYYY-MM-DD), so schema coverage is 100% and the baseline is 3. The description adds no format hints, range limits, or timezone considerations beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb (查詢 / query whether) and a precise resource (whether a given Taiwan date is a holiday), with the scope narrowed further to include 補假 and 補班 days. It is understandable on its own, but it never mentions the closely related sibling life__get_holidays or trip__taiwan_holidays, so an agent cannot tell from the description alone which of those to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance and no alternatives are named. Usage is only inferable from the single-purpose phrasing, which is weak given multiple holiday-related siblings (life__get_holidays, trip__taiwan_holidays, travel__get_2027_long_weekends) exist.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full load, but 'reads robots.txt' does convey that this is a non-mutating lookup and it names the scope (13 crawler user-agents). It omits edge-case behavior such as what is reported when robots.txt is absent or unreachable, or whether requests are rate-limited.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One front-loaded sentence with the output scope (allow vs block per crawler) stated immediately and no filler. The bracketed '［llms.txt 檢查工具］' tag is mildly redundant with the tool name but does not bloat it.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations, so the description must be complete on its own; it does say the result is a per-crawler allowed/blocked report, which is the key return shape. Missing the meaning of the url argument and the missing-robots.txt case leaves gaps for a tool this simple.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single 'url' parameter has no description anywhere. 'Reads the website robots.txt' hints the url is a site URL, but it is ambiguous whether a full robots.txt path, a page URL, or a bare domain is expected – the description should resolve this and does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb (reads robots.txt) and resource (AI crawler allow/block status) and enumerates the crawlers checked (GPTBot, ClaudeBot, etc.), so the agent knows exactly what comes back. It does not, however, distinguish itself from near-identical siblings such as seo__check_site_crawlers or seo__test_robots_txt.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative-tool guidance is given. The description never tells the agent why it would pick this over seo__check_site_crawlers or seo__test_robots_txt.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool performs multiple checks and returns a 0–100 score with improvement suggestions, and the word 'check' implies a read-only operation. However, it does not state whether it accesses the live site, needs authentication, has rate limits, or whether results are cached.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence prefixed by a bracketed label, and it front-loads the core purpose. It contains no filler, though the enumeration of checks makes it slightly long for a one-input tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter audit tool with full schema coverage and no annotations, the description covers the scope and the return shape (0–100 score plus suggestions). It is complete enough to call correctly, though it provides no detail about the output structure or how the score is composed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the sole parameter is already documented as 'website URL or domain'. The description adds no further parameter meaning, which is acceptable given the high schema coverage, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: checking llms.txt and related AI-friendliness signals for a website. It clearly describes the scope, including llms.txt format, llms-full.txt, Markdown pages, robots.txt AI crawler settings, sitemap, and a score. It does not explicitly distinguish itself from sibling tools like validate_llms_txt or check_ai_crawler_access, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool checks but offers no guidance on when to use it versus alternatives. Sibling tools such as llms__validate_llms_txt and llms__check_ai_crawler_access are not mentioned, and there are no exclusion conditions or recommended contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations, so the description carries the full burden. It discloses that the tool fetches the homepage and sitemap, which is genuine behavioral context, but says nothing about network behavior, timeouts, failure modes, or where/how the generated draft is returned. For a tool that performs live crawling, this is thin.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single efficient sentence with the source data front-loaded before the outcome. No waste, though the bracketed category label consumes space without adding operational information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a generation tool with no annotations, no output schema, and an undocumented parameter, the description is under-specified. An agent cannot tell what the produced llms.txt draft contains, how it is returned, or how it differs in result from the check/validate siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (url) with 0% schema description coverage. The description implies the url is the target website whose homepage and sitemap are read, which adds modest meaning, but gives no format guidance (scheme, trailing slash, subpaths) and no constraints. Baseline is roughly met but not exceeded.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb (generate) and resource (llms.txt), plus the data sources it reads (homepage and sitemap). It implicitly separates itself from the llms__check_llms_txt and llms__validate_llms_txt siblings, though it never names them explicitly. The bracketed label '檢查工具' (checking tool) is slightly at odds with the generate verb but doesn't mislead.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance at all. With three closely related siblings in the llms__ group (check_llms_txt, validate_llms_txt, check_ai_crawler_access), the description should say when to generate a new draft versus when to check or validate an existing one. Nothing is offered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It reveals which structural elements are checked but says nothing about the nature of the result (pass/fail, list of violations), whether validation is strict or heuristic, or any failure behavior. This is a thin disclosure for an annotation-free tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the tool's scope front-loaded in a bracketed tag. No wasted words, though the tag is mildly redundant with the name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter validator with no output schema and no annotations, the description covers the input and the checked elements but omits the return shape, which matters for a validation tool. Adequate but with a clear gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single required parameter 'content' is undocumented in the schema. The description partially compensates by implying the input is a block of llms.txt text to be validated, but it adds no format, size, or encoding detail beyond that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (驗證/validate) and resource (llms.txt 內容) and enumerates what is checked (H1, 摘要, 分區, 連結). However, it does not distinguish itself from the sibling llms__check_llms_txt, leaving ambiguity between the two near-identical tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this versus llms__check_llms_txt or llms__check_ai_crawler_access. Usage is only implied by the word 'validate'. An agent has no criteria for choosing between the overlapping sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full disclosure burden, and it does add one real behavioral fact: besides the checklist it also returns site-wide reduced-motion CSS. It does not say whether output is static text, whether it inspects a URL, or anything about read-only/risk profile, though the zero-param nature limits risk.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence, front-loaded with the content type and then the covered standards, with no filler prose. The '[網頁動畫工具箱]' bracket prefix is mild overhead that does not aid selection.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless, guidance-style tool with no output schema, listing the standards covered and the bundled reduced-motion CSS is enough for an agent to know the shape of the result. It could state whether the CSS is template/scaffold versus site-specific, but the essentials are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is no parameter semantics to document; per the baseline that yields a 4. The description correctly adds no invented arguments.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource — an accessibility and performance checklist for web animations — and enumerates the exact criteria covered (WCAG 2.2.2, 2.3.1, 2.3.3, prefers-reduced-motion, CLS, LCP), which clearly separates it from the code-generating siblings (generate_easing, generate_keyframes). It is a content-label rather than a verb+object statement, but an agent can tell what it produces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: the checklist framing suggests you call it when auditing web animations for a11y/perf, but there is no explicit when-to-use, prerequisite, or named alternative among the motion__ siblings. Adequate but leaves routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose scope by enumerating exactly which features are covered (View Transitions, scroll-driven animations, linear(), @starting-style, Web Animations API), which is real behavioral information. However it says nothing about the shape of the response, freshness of the compatibility data, or whether coverage is partial, leaving meaningful gaps for an informational tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the feature enumeration front-loaded and no padding. The bracketed category prefix is slightly redundant meta-text, but nothing else wastes space.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description must explain what comes back. It communicates the topic and the covered features but not the return form (browser-by-browser version table, baseline status, or note), so an agent can pick the tool correctly yet not know what to expect from the call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters and schema coverage is 100%, so there is nothing for the description to disambiguate. Baseline 4 applies for a no-parameter tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (browser support versions for five specific web animation features) and enumerates the covered features, so scope is visible. But there is no clear verb — it never says whether it returns a per-browser support table, baseline dates, or general guidance — and the bracketed category tag '[Web Animation Toolbox]' substitutes for a definition rather than supplying one. It is viable but vaguer than a 4 requires.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative is given. Siblings such as motion__compare_animation_libraries, motion__animation_a11y_checklist, and motion__generate_easing exist and are never mentioned, so an agent must infer from the feature list alone that this is the browser-compatibility lookup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden. It does not state the return format (presumably a comparison table), nor whether the data is static reference content, but the tool takes zero parameters and cannot mutate anything, so the practical risk of missing behavioral detail is low.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with a category tag, a verb, and an enumerated scope — no filler or repetition. It is dense but every clause carries information an agent can use.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only lookup with no output schema, the description is adequate to call the tool correctly. It is thin, however, on what the agent actually gets back and on where this sits relative to the other motion__ reference tools, which is the main remaining gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so there is nothing for the description to document; baseline 4 applies. The description correctly implies the tool is fully self-contained with no filtering or configuration inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (比較/compare) plus the resource (web animation libraries) and enumerates the exact subjects covered (CSS, Web Animations API, GSAP, Motion, Anime.js, Lottie, Rive, Three.js, Lenis) along with the comparison axes (licensing and applicable scenarios). This lets an agent separate it from other motion__ tools like generate_easing or animation_browser_support, though it never explicitly names those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase '適用情境' (applicable scenarios) implies the tool is for choosing an animation tool for a project, which gives an agent implied context. However there is no explicit when-to-use statement, no prerequisites, and no guidance on alternatives to reach for instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose the return payload (CSS output, Web Animations API sample, sampling points), which is useful, but says nothing about control-point valid ranges, validation errors, or what happens when both name and points are supplied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence that front-loads the toolbox category, the action, and the two input modes. It is efficient, though the packed parenthetical of preset names is slightly heavy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema the description helpfully summarizes the returned artifacts, but with 5 undocumented parameters, no annotations, and no stated defaults or mode exclusivity, an agent still lacks enough to call it confidently in edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and none of the 5 parameters are documented, so the description must compensate. It explains that a preset name (with examples and a count of 32) or four control points can be used, which maps loosely onto the name/x1/y1/x2/y2 fields, but it never states the ordering, ranges, or that the two modes are alternatives.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (generate) and resource (CSS cubic-bezier easing curves), plus the two input modes (a named preset among 32, or four control points) and the payload returned. It is clearly distinguishable from siblings like generate_keyframes and spring_to_css_linear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the two input modes, but the description never states when to pick this over the related motion siblings (spring_to_css_linear, generate_keyframes) or whether name and control points are mutually exclusive. Context exists but no exclusions or alternatives are named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It discloses that output is CSS and can include a flowing animation or gradient text, but says nothing about return format, defaults, ranges for angle/speed, or whether the tool is purely computational with no side effects. That gap is significant for a 5-parameter generator.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A short, front-loaded sentence states the core capability first, then a compact clause covers the colors format. No filler. The only weakness is that it packs the parameter note into the same breath, which slightly muddies the structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations, no output schema, and five parameters at 0% schema coverage, the description should explain the returned CSS artifact and the semantics/defaults of angle and speed. It does neither, so an agent cannot confidently invoke the tool beyond the required colors argument.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds real meaning for colors (comma-separated HEX), the type parameter (linear/radial/conic), and animate (optional flowing animation), but leaves angle and speed completely unexplained with no units or ranges. Partial compensation only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource – generating CSS gradients – and enumerates the three gradient families (linear, radial, conic) plus optional animation and gradient-text output. That is enough to separate it from siblings like motion__generate_easing or motion__generate_keyframes. It stops short of explicitly naming what it is not, so a 5 is not warranted.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The text describes what the tool can produce but never says when to reach for it versus the other motion tools. There is no statement of prerequisites, typical input scenarios, or exclusions. Only implied usage from the feature list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose one real behavioral trait — the output includes a 減少動態 (prefers-reduced-motion) mode — which is useful. However it says nothing about the return shape, whether duration/delay are in ms or s, or how iterations behaves, so the disclosure is thin for a generator with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact segments with the core action front-loaded before the preset list. The bracket prefix is slightly redundant but costs little, and the long preset enumeration earns its place by filling a schema gap.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a five-parameter generator with no annotations and no output schema, the description omits too much: units and ranges for delay/duration, easing format, iterations semantics (e.g. 'infinite'), and what the returned keyframe string looks like. Only the preset values are fully covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and there are no enums in the schema, so the description must compensate for all five params. It enumerates the 15 valid preset values (genuinely valuable since the schema types preset as a bare string), but delay, duration, easing, and iterations are left completely unexplained — no units, format, or defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (產生) and resource (CSS @keyframes 動畫), and notes it includes a reduced-motion variant. This clearly separates it from siblings like motion__generate_easing or motion__generate_gradient, though it never names those alternatives directly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use or when-not guidance is given beyond the toolbox tag. An agent cannot tell from the text when to pick this versus motion__generate_easing, motion__spring_to_css_linear, or motion__compare_animation_libraries. The preset enumeration is data, not routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It says the tool produces a CSS linear() easing function and a suggested duration, which is useful output context, but it does not disclose defaults, valid ranges, units, or what happens when parameters are omitted (the schema marks none as required).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence with a bracketed category tag followed by the core action; no filler or repetition. It is efficient, though the bracket tag adds little beyond classification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations, no output schema, and 0% parameter coverage, the description should do more: it omits default behavior for the unrequired parameters, valid input ranges, and the shape of the returned CSS/duration value. What is present is accurate but insufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for all three parameters. It names them (stiffness, damping, mass) and frames them as spring physics values, but adds no units, ranges, defaults, or interaction semantics beyond what the bare schema property names already convey.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it simulates spring physics parameters (stiffness, damping, mass) and converts them into a CSS linear() easing function plus a suggested duration. This is far more precise than a tautology and an agent can tell what it produces. It does not, however, differentiate itself from the sibling motion__generate_easing, which plausibly overlaps in purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus alternatives such as motion__generate_easing or motion__generate_keyframes, and no prerequisites or exclusions. The intended context (spring-based motion design) is only implied by the parameter names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It says it generates commands and HTML syntax, implying it does not execute ffmpeg itself, but it does not state output format, validation behavior, side effects, permissions, or whether any files are read or written.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. It is concise, though extremely terse given the tool's parameter complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 6 undocumented parameters, no annotations, and no output schema. The description omits parameter meanings, output format, and usage details, making it inadequate for correct invocation without opening the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are 6 parameters with 0% schema description coverage, so the description must compensate. It mentions codecs and 'poster', but none of the actual parameters (fps, audio, input, width, output, quality) are explained or mapped to meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (產生/generate) and resource (ffmpeg 指令, HTML video 語法) for web background video compression, naming supported codecs (H.264, VP9, AV1, poster). This clearly distinguishes it from unrelated sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through '網頁背景影片壓縮', giving context for when the tool is useful. However, it offers no explicit when-not conditions or alternative tools, and no prerequisites or input requirements are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden but discloses meaningful scope: English-language only, Standard format only, and a dated snapshot (2026-07-16 version). It does not state read-only nature, output format, or whether other formats exist, but for a zero-parameter lookup this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with no redundant filler; the category tag, verb, resource, format and version all fit in one line and nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No annotations and no output schema, so the description must stand alone; it conveys what content is returned (Standard banlist and banned battlefields, dated) but says nothing about the response shape or whether non-Standard formats are covered. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes no parameters, so there is nothing for the description to disambiguate; the 4 baseline applies. The description correctly implies a fixed, parameterless lookup.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (取得/get) and resource (符文戰場英文版 Standard 禁卡表與禁用戰場), plus the format and version date, which goes beyond the bare tool name. It is clearly distinguishable from sibling rule-lookup tools like riftbound__search_rules or riftbound__lookup_term, though it does not name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance, and no mention of alternatives such as search_rules or get_faq. Usage is only implied by the topic (checking what is banned), with no exclusions or routing hints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It usefully discloses the return format (Markdown full text), which is genuine context beyond the schema, but says nothing about permission needs, behavior on an invalid slug, or whether the content is static/cached. Modest but non-zero added value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with a bracketed scope label, verb, resource, and format. No waste and no padding, though the bracket prefix is mildly redundant with the verb phrase that follows.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter document fetcher with no output schema and a self-documenting enum, the description covers what it returns, in what format, and for which resource. Only the invalid-slug/error behavior is unaddressed, which is minor at this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never mentions the slug parameter's form. However, the enum supplies the complete set of 22 valid values (preface, why-now, ch01–ch18, glossary, notes), so the semantic space is largely self-documenting despite the prose adding nothing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb (取得/get) plus resource (符文戰場新手指南某一章全文 – full text of one chapter of the Riftbound beginner's guide), which is clearly distinguishable from siblings like get_banlist, get_faq, and search_rules. It also states the output format (Markdown). It stops short of explicitly naming a sibling it is not, so it is clear but not fully differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no exclusion criteria, and no reference to the alternative Riftbound tools (search_rules, lookup_term, get_faq) that could also answer an agent's question. Usage is only inferable from the single-purpose name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says nothing about what the FAQ covers, whether results are static/cached, or how the content is structured. For a harmless zero-parameter read this is low risk, but it is still minimal disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence, front-loaded with a bracketed category label, with no wasted text. Minor redundancy in repeating 符文戰場 twice, but it stays efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple zero-parameter tool with no output schema, the description conveys the gist (beginner FAQ Q&A). However, it does not indicate what topics the FAQ covers or how it differs from the other riftbound knowledge tools, leaving a small but real gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the schema is trivially complete and there is nothing for the description to clarify. Baseline 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (取得/get) and resource (新手常見問題與答案/beginner FAQ), so an agent knows exactly what it retrieves. It does not explicitly contrast itself with the other riftbound tools (search_rules, lookup_term, get_banlist), but the FAQ resource is clearly distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no exclusions, and no mention of the sibling tools that would define its boundary (e.g., use lookup_term for single term definitions vs. this for broader new-player FAQ). The agent must infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral load. It does disclose the return content (Chinese translation plus explanation), but says nothing about behavior for unknown/ambiguous terms, whether Chinese input is accepted, or any rate/permission constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with a bracketed category tag up front and examples at the end. No wasted words, though the tag consumes some of the limited space.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter lookup with no output schema and no annotations, the description covers what is returned and example inputs, but leaves gaps around edge cases (unknown terms, Chinese vs English input) that an agent would need to handle.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single 'term' parameter is undocumented in the schema; the description partially compensates by giving example English card-face terms, clarifying the expected input format, but the parameter is not fully specified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (查) and resource (符文戰場英文卡面名詞的中文譯名與說明), with concrete examples (Might, Rune, Conquer) that pin down the domain. It is distinguishable from riftbound siblings like get_faq or search_rules, though it does not explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The '[新手指南]' tag and the example terms imply the context (encountering an English Riftbound card term), but there is no explicit when-to-use statement and no comparison to glossdary-style siblings such as glossary__explain_term or riftbound__search_rules.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses useful behavioral facts: the search covers the English beginner's guide, output is in Traditional Chinese, and the return shape is chapters and paragraphs. However it says nothing about permissions, matching behavior (keyword vs semantic), result limits, or whether it is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One compact sentence with the category tag front-loaded and no filler. It is slightly dense with parenthetical qualifications, but every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter search tool with no output schema, the description covers purpose, language, and return unit. The main gap is routing relative to the other riftbound retrieval tools, which is exactly what an agent needs to make the right call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single query parameter, so the schema already documents it with examples (Hold, sideboard). The description adds no additional parameter meaning beyond restating that it is a search, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (search Riftbound TCG rules commentary in the beginner's guide) and adds scope details (English guide, Traditional Chinese output, returns chapters and paragraphs). It is clear what the tool does, but it never contrasts itself with the sibling tools riftbound__get_chapter, riftbound__get_faq, or riftbound__lookup_term, so an agent cannot easily tell which riftbound retrieval tool to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no exclusions, and no mention of alternatives even though three sibling riftbound tools (get_chapter, get_faq, lookup_term) target overlapping content. The agent must infer the selection logic entirely on its own.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden. It usefully enumerates what data the tool surfaces (production, release schedule, postponement history, trailers, news) and notes multilingual query support, but it says nothing about permissions, rate limits, or what happens on a failed/ambiguous match. Read-only behavior is only implied by the lookup verb.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense but well-formed sentence, front-loaded with the database scope tag followed by the action and the list of returned data. Every clause adds information; the enumeration of fields is long but earns its place given there is no output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description does the heavy lifting by listing the returned data categories and accepted query languages. It is nearly complete for a lookup tool; only failure/ambiguity behavior and any access constraints are unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single 'q' parameter has no description, so the description must compensate. It does so well: it states that Chinese, English and Japanese titles are all accepted and gives concrete examples (沙丘3, Avengers Doomsday, 薬屋のひとりごと), making the input format unambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('查單一作品' – look up a single title) and a concrete resource, bracketed with the database scope (release dates and trailers). It is clearly a single-title lookup, though it does not explicitly contrast itself with the sibling screen__lookup_any_title, so an agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by '查單一作品' – one title per call – but there is no explicit when-to-use/when-not guidance and no routing to alternatives such as screen__list_titles or screen__lookup_any_title. The agent can infer intent but is not told how to choose among the many sibling screen tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the dataset scope (2026–2027 key titles) and some returned fields, which is useful behavioral context, but omits pagination behavior, result limits, sorting, and authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is a single dense sentence that front-loads the database label and then states the listing behavior, contents, and filters. It is efficient, though the bracketed tag adds minor extra framing before the main verb.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.